ISSN 2320 - 2602 Volume 3, No.5, May 2014 Suvajit Dutta et al., International Journal Advances in Computer Science Science and Technology, May 2014, 325 - 330 International Journal ofofAdvances in Computer and 3(5), Technology
Available Online at http://warse.org/pdfs/2014/ijacst04352014.pdf
A Cryptography Algorithm Using the Operations of Genetic Algorithm & Pseudo Random Sequence Generating Functions Suvajit Dutta1, Tanumay Das2, Sharad Jash3, Debasish Patra4, Dr.Pranam Paul5 1
Student, MCA, Narula Institute of Technology, Kolkata, West Bengal, INDIA,
[email protected] Student, MCA, Narula Institute of Technology, Kolkata, West Bengal, INDIA,
[email protected] 3 Student, MCA, Narula Institute of Technology, Kolkata, West Bengal, INDIA,
[email protected] 4 Student, MCA, Narula Institute of Technology, Kolkata, West Bengal, INDIA,
[email protected] 5 Computer Application, Narula Institute of Technology, Kolkata, West Bengal, INDIA,
[email protected] 2
ABSTRACT In today’s computer world security, integrity, confidentiality of the organization’s data is the most important issue. This paper deals with the confidentiality of electronic data which is transmitted over the internet. Data encryption is widely used to ensure security of the data. Genetic algorithms (GAs) are a class of optimization algorithms. Many problems can be solved using genetic algorithms through modeling a simplified version of genetic processes. In our approach we are using the concept of genetic algorithms with pseudorandom function to encrypt and decrypt data stream. The encryption process is applied over a binary file so that the algorithm can be applied over any type of txt as well as multimedia data.
decryption, known as the private key. Asymmetric cryptography is often used in key distribution and digital signature for its slow processing speed. The symmetric cryptography is normally used to encrypt private data for its high performance. There have been various data encryption techniques on multimedia data proposed. Genetic Algorithms (GAs) are among such techniques. The genetic algorithm is a search algorithm based on the mechanics of natural selection and natural genetics. The genetic algorithm belongs to the family of evolutionary algorithms, along with genetic programming, evolution strategies, and evolutionary programming. The set of operators usually consists of mutation, crossover and selection. In our work we are using crossover and mutation operation.
KEYWORDS Encryption, Decryption, Genetic algorithm, Mutation, Pseudorandom Sequence, symmetric Key, Cryptography.
Crossover: Crossover operator has the significance as that of crossover in natural genetic process. In this operation two chromosomes are taken and a new is generated by taking some attributes of first chromosome and the rest from second chromosome. In GAs a crossover can be of following types:
1. INTRODUCTION In the advent of greater demand in digital signal transmission in recent time, the problem of huge losses from illegal data access has become a burning issue. Accordingly, the data security has become a critical and imperative issue in multimedia data transmission applications. In order to protect valuable information from undesirable users or against illegal reproduction and modifications, various types of cryptographic schemes are needed.
1. Single Point Crossover: In this crossover, a random number is selected from 1 to n as the crossover point, where n being the number of chromosome. Any two chromosomes are taken and operator is applied. 2. Two Point Crossover: In this type of crossover, two crossover points are selected and the crossover operator is applied.
There are two types of cryptographic schemes: symmetric cryptography and asymmetric cryptography. The symmetric scheme uses the same key for encryption and decryption. Two keys is used In asymmetrical cryptography, one for encryption, known as the public key, and the other for
3. Uniform Crossover: In this type, bits are copied from both chromosomes uniformly. 325
Suvajit Dutta et al., International Journal of Advances in Computer Science and Technology, 3(5), May 2014, 325 - 330 Mutation: Mutation is a genetic operator used to maintain 2. PROPOSED METHOD genetic diversity from one generation of population to the The block diagram of Figure1 shows the encryption next. It is similar to biological mutation. Mutation allows process. The diagram consists of Crossover, Mutation and the algorithm to avoid local minima by preventing the Pseudorandom Number Sequence. Each module will be population chromosomes from becoming too similar to each discussed in the following sub section: other. GAs involves string based modifications to the elements of a candidate solution. These include bit-reversal 2.1 Pseudorandom Sequence in bit-string GAs. PRNG used to generate a Pseudorandom Sequence of numbers for our encryption and decryption method. In our method we are using two sequence and the values needed to generate those sequence are stored as Secret key.
Pseudorandom number generator: A pseudorandom number generator is an algorithm for generating a sequence of numbers that approximates the properties of random numbers. The sequence is not truly random in that it is completely determined by a relatively small set of initial values, called the PRNG’s state. A PRNG suitable for Cryptographic applications is called a cryptographically secure PRNG (CSPRNG).
2.2 Crossover In our method we are using three type of crossover operation (Single point, Two Point, Uniformed). Modulating the Pseudorandom Sequence by 3 the crossover operations will executed according to the sequence. The process of this crossovers is showed in the figures:
Some PRNG files are Blum Blum Shub, Wichmann-Hill, Complementary-multiply-with-carry, Inversive congruential generator, ISAAC (cipher), Lagged Fibonacci generator, Multiply-with-carry, Naor-Reingold Pseudorandom Function, Park–Miller random number generator, RC4 PRGA, Well Equidistributed Long-period Linear, Xorshift, Mersenne twister, Sophie Germain prime. In this paper, we propose a new approach for encrypting binary files.
Figure 2: Single Point Crossover
Figure 3: Two Point Crossover
Figure 4: Uniformed Crossover Figure 1: Flowchart of the encryption process
2.3 Mutation
We have shown the encryption process briefly in the Figure.1.
For Mutation operation we are using flip bit mutation technique (i.e. if the genome bit is 1,it is changed to 0 and vice versa). Mutation operation will be applied on one of the binary data block. The index number of that data block is stored as Secret key.
The rest of the paper is organized as follows. In Section 2, we introduce the proposed method. Section 3discusses the experimental results. Section 4, discusses about the analysis of the proposed method. Section 5 concludes the paper. 326
Suvajit Dutta et al., International Journal of Advances in Computer Science and Technology, 3(5), May 2014, 325 - 330 Do Mutation of the mentioned block number in the key: 2.4 Key Structure Ei =255-(255-Ei) In the algorithm we have basically used five keys: one is for 3) Generate a pseudorandom sequence and modulate the dividing the plane text into blocks. Second is for generating sequence with the secret key, pseudorandom sequence for crossover operation. Third is We denote the sequence as: for generating another pseudorandom sequence, fourth key Z1, Z2, Z3….Zn is for modulate the sequence (Range 16 - 255) and last one Find out the decimal value of each crossover blocks i.e.: is for mutation operation. E1, E2, E3…. En Now do the following operation, 2.5 The Encryption Process Xi=Zi Zi
